The general Sci2 Tool user interface is shown in Figure 2.3.

Figure 2.3: Sci2 Tool Interface Components

2.2.1 Menus

The Sci2 Tool menu structure is arranged such that a workflow runs from left to right. The 'File' menu on the left allows a user to load data in a number of formats, which can then be prepared, preprocessed, analyzed, and finally visualized. Users also have the option of modeling new networks or finding help online. File

In the 'File' menu, the first option after clicking on 'Load' is 'Select a File':

Figure 2.4: Using 'Load' to select a file in Sci2

The 'File' menu functionality includes loading multiple data formats (see section 2.3 Data Formats for details), loading ISI and NSF data, saving and viewing results, and merging or splitting node and edge files.

The 'Console' window documents all operations performed on the data.

Figure 2.6: Sci2's Console and Schedule after successfully loading a database file

When the scheduler indicates that the 'Load' operation is complete, the .isi file will appear in the data manager, preceded by a database icon. The converter graph and directory reader produces a sample graph based on filetypes supported by the Sci2 Tool and a sample tree based on any directory structure on the hard drive, respectively. Data Preparation

The screen shots in the rest of the section are from the extended version of the Sci2 Tool. To extend Sci2 see 3.2 Additional Plugins.

After loading a file, use options in the 'Data Preparation' menu to clean the data and create networks or tables which can be used in the preprocessing, analysis, and visualization steps. The options in the top of the menu are for any table-based datasets (like csv files) and are used to extract networks. The 'Data Preparation > Database' menu is specifically for ISI or NSF data previously loaded into a database.  Find detailed information on each menu item in section 3.1 Sci2 Algorithms and Tools.

Figure 2.7: Data Preparation options Preprocessing

Use preprocessing algorithms to prune or append networks or tables before analyzing and visualizing them. The menu is separated by domain, and most simple tasks require staying within the same domain. For example, to visualize a co-authorship network, only use algorithms within the 'Networks' domain under 'Preprocessing', 'Analysis', and 'Visualization'. Similarly, a geographic map requires only 'Geospatial' algorithms. Find detailed information on each menu item in section 3.1 Sci2 Algorithms and Tools.

Figure 2.8: Preprocessing options Analysis

Once data is loaded, prepared, and processed with whatever features needed, analysis is possible in each of the four domains: temporal, geospatial, topical, or network.

Figure 2.9: Analysis options
Analysis results can be used on their own or in conjunction with visualizations to gain insight into a dataset. The Sci2 Tool features predominantly network analysis algorithms, however the tool also supports geocoding of table data and burst analysis for topical or temporal studies. Find detailed information on each menu item in section 3.1 Sci2 Algorithms and Tools Modeling

The Sci2 Tool supports the creation of new networks via pre-defined models. Learn more about modeling in section 4.10 Modeling (Why?).

Figure 2.10: Modeling options Visualization

Once all previous data steps are complete, the Sci2 Tool can visualize the results. The most popular choice for visualizing networks is the GUESS toolkit, or DrL for much larger scale networks. Geocoded data can be represented on a map of the world or the United States, and temporal or topical data can be viewed using the horizontal bar graph. Find detailed information on each menu item in section3.1 Sci2 Algorithms and Tools.

Figure 2.11: Visualization options

Cytoscape should be explained here.  Coming soon. Help

The 'Help' menu leads to online documentation, advanced tool configuration, and detailed development information.

Figure 2.12: Help options

2.2.2 Console

All operations such as loading, viewing, or saving datasets, running various algorithms, and algorithm parameters, etc. are logged sequentially in the 'Console' window as well as in log files stored in the 'yoursci2directory /logs' directory. The Console window also displays the acknowledgement information about the original authors of the algorithm, the developers, the integrators, a reference paper, and the URL to the reference if available, together with an URL to the algorithm description in the NWB/Sci2 community wiki.

2.2.3 Data Manager

The 'Data Manager' window displays all currently loaded and available datasets. The type of a loaded file is indicated by its icon:
Text – text file
Table – tabular data (csv file)
Matrix-data (Pajek .mat)
Plot – plain text file that can be plotted using Gnuplot
Network – Network data (in-memory graph/network object or network files saved as Graph/ML, XGMML, NWB, Pajek .net or Edge list format)
Database – In-memory database
Tree – Tree data (TreeML)
Derived datasets are indented under their parent datasets. That is, the children datasets are the results of applying certain algorithms to the parent dataset.

2.2.4 Scheduler

The 'Scheduler' lets users keep track of the progress of running algorithms.